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aaf0480e9c
The lumped losses are used in the computation of the loss/gain profile through the fiber whether the Raman effect is considered or not. The computed power profile is used to calculate the related NLI impairment. Using the 'gn_model_analytic' method, the lumped losses are taken into account as the contribution of an additional total loss at the end of the fiber span. In case the 'ggn_spectrally_separated' is selected, the method uses the computed power profile according to the specified z and frequency arrays. The lumped losses are so considered within the NLI power evolution along the fiber. Change-Id: I73a6baa321aca4d041cafa180f47afed824ce267 Signed-off-by: Jan Kundrát <jan.kundrat@telecominfraproject.com>
129 lines
6.0 KiB
Python
129 lines
6.0 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Checks that RamanFiber propagates properly the spectral information. In this way, also the RamanSolver and the NliSolver
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are tested.
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"""
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from pathlib import Path
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from pandas import read_csv
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from numpy.testing import assert_allclose
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from numpy import array, genfromtxt
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import pytest
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from gnpy.core.info import create_input_spectral_information, create_arbitrary_spectral_information
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from gnpy.core.elements import Fiber, RamanFiber
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from gnpy.core.parameters import SimParams
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from gnpy.tools.json_io import load_json
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from gnpy.core.exceptions import NetworkTopologyError
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from gnpy.core.science_utils import RamanSolver
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TEST_DIR = Path(__file__).parent
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def test_fiber():
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""" Test the accuracy of propagating the Fiber."""
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fiber = Fiber(**load_json(TEST_DIR / 'data' / 'test_science_utils_fiber_config.json'))
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# fix grid spectral information generation
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spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15,
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baud_rate=32e9, power=1e-3, spacing=50e9)
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# propagation
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spectral_info_out = fiber(spectral_info_input)
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p_signal = spectral_info_out.signal
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p_nli = spectral_info_out.nli
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expected_results = read_csv(TEST_DIR / 'data' / 'test_fiber_fix_expected_results.csv')
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assert_allclose(p_signal, expected_results['signal'], rtol=1e-3)
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assert_allclose(p_nli, expected_results['nli'], rtol=1e-3)
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# flex grid spectral information generation
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frequency = 191e12 + array([0, 50e9, 150e9, 225e9, 275e9])
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slot_width = array([37.5e9, 50e9, 75e9, 50e9, 37.5e9])
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baud_rate = array([32e9, 42e9, 64e9, 42e9, 32e9])
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signal = 1e-3 + array([0, -1e-4, 3e-4, -2e-4, +2e-4])
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spectral_info_input = create_arbitrary_spectral_information(frequency=frequency, slot_width=slot_width,
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signal=signal, baud_rate=baud_rate, roll_off=0.15)
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# propagation
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spectral_info_out = fiber(spectral_info_input)
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p_signal = spectral_info_out.signal
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p_nli = spectral_info_out.nli
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expected_results = read_csv(TEST_DIR / 'data' / 'test_fiber_flex_expected_results.csv')
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assert_allclose(p_signal, expected_results['signal'], rtol=1e-3)
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assert_allclose(p_nli, expected_results['nli'], rtol=1e-3)
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@pytest.mark.usefixtures('set_sim_params')
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def test_raman_fiber():
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""" Test the accuracy of propagating the RamanFiber."""
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# spectral information generation
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spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15,
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baud_rate=32e9, power=1e-3, spacing=50e9)
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SimParams.set_params(load_json(TEST_DIR / 'data' / 'sim_params.json'))
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fiber = RamanFiber(**load_json(TEST_DIR / 'data' / 'test_science_utils_fiber_config.json'))
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# propagation
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spectral_info_out = fiber(spectral_info_input)
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p_signal = spectral_info_out.signal
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p_ase = spectral_info_out.ase
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p_nli = spectral_info_out.nli
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expected_results = read_csv(TEST_DIR / 'data' / 'test_raman_fiber_expected_results.csv')
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assert_allclose(p_signal, expected_results['signal'], rtol=1e-3)
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assert_allclose(p_ase, expected_results['ase'], rtol=1e-3)
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assert_allclose(p_nli, expected_results['nli'], rtol=1e-3)
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@pytest.mark.parametrize(
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"loss, position, errmsg",
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((0.5, -2, "Lumped loss positions must be between 0 and the fiber length (80.0 km), boundaries excluded."),
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(0.5, 81, "Lumped loss positions must be between 0 and the fiber length (80.0 km), boundaries excluded.")))
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@pytest.mark.usefixtures('set_sim_params')
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def test_fiber_lumped_losses(loss, position, errmsg, set_sim_params):
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""" Lumped losses length sanity checking."""
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SimParams.set_params(load_json(TEST_DIR / 'data' / 'sim_params.json'))
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fiber_dict = load_json(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber_config.json')
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fiber_dict['params']['lumped_losses'] = [{'position': position, 'loss': loss}]
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with pytest.raises(NetworkTopologyError) as e:
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Fiber(**fiber_dict)
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assert str(e.value) == errmsg
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@pytest.mark.usefixtures('set_sim_params')
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def test_fiber_lumped_losses_srs(set_sim_params):
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""" Test the accuracy of Fiber with lumped losses propagation."""
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# spectral information generation
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spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15,
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baud_rate=32e9, power=1e-3, spacing=50e9)
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SimParams.set_params(load_json(TEST_DIR / 'data' / 'sim_params.json'))
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fiber = Fiber(**load_json(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber_config.json'))
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raman_fiber = RamanFiber(**load_json(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber_config.json'))
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# propagation
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# without Raman pumps
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stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(
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spectral_info_input, fiber)
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power_profile = stimulated_raman_scattering.power_profile
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expected_power_profile = genfromtxt(TEST_DIR / 'data' / 'test_lumped_losses_fiber_no_pumps.csv', delimiter=',')
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assert_allclose(power_profile, expected_power_profile, rtol=1e-3)
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# with Raman pumps
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expected_power_profile = genfromtxt(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber.csv', delimiter=',')
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stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(
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spectral_info_input, raman_fiber)
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power_profile = stimulated_raman_scattering.power_profile
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assert_allclose(power_profile, expected_power_profile, rtol=1e-3)
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# without Stimulated Raman Scattering
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expected_power_profile = genfromtxt(TEST_DIR / 'data' / 'test_lumped_losses_fiber_no_raman.csv', delimiter=',')
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stimulated_raman_scattering = RamanSolver.calculate_attenuation_profile(spectral_info_input, fiber)
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power_profile = stimulated_raman_scattering.power_profile
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assert_allclose(power_profile, expected_power_profile, rtol=1e-3)
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